no longer require SliceSource instances, renamed slice_obj -> slice_item, updated docs

Author: forman

CHANGES.md

@@ -4,6 +4,11 @@
   target dataset. An existing target dataset (and its lock) will be
   permanently deleted before appending of slice datasets begins. [#72]
 
+* Simplified writing custom slice sources. The configuration setting
+  `slice_source` can now be any function that returns an `xarray.Dataset`
+  or any object that can be used to finally yield an `xarray.Dataset`: 
+  `str` (local file path or URI), or a `SliceSource` instance. [#78]
+
 * Internal refactoring: Extracted `Config` class out of `Context` and 
   made available via new `Context.config: Config` property.
   The change concerns any usages of the `ctx: Context` argument passed to

docs/api.md

@@ -5,20 +5,8 @@ All described objects can be imported from the `zappend.api` module.
 ## Function `zappend()`
 
 ::: zappend.api.zappend
-    options:
-        show_root_heading: true
-
-## Function `to_slice_factories()`
-
-::: zappend.api.to_slice_factories
-    options:
-        show_root_heading: true
-
-## Function `to_slice_factory()`
-
-::: zappend.api.to_slice_factory
-    options:
-        show_root_heading: true
+options:
+show_root_heading: true
 
 ## Class `SliceSource`
 
@@ -39,22 +27,21 @@ All described objects can be imported from the `zappend.api` module.
 ## Types
 
 ::: zappend.api.SliceObj
-    options:
-        show_root_heading: true
-
-::: zappend.api.SliceFactory
-    options:
-        show_root_heading: true
+options:
+show_root_heading: true
 
+::: zappend.api.SliceCallable
+options:
+show_root_heading: true
 
 ::: zappend.api.ConfigItem
-    options:
-        show_root_heading: true
+options:
+show_root_heading: true
 
 ::: zappend.api.ConfigList
-    options:
-        show_root_heading: true
+options:
+show_root_heading: true
 
 ::: zappend.api.ConfigLike
-    options:
-        show_root_heading: true
+options:
+show_root_heading: true

docs/guide.md

@@ -534,9 +534,38 @@ passed using the optional `target_storage_options` setting.
 
 ### Slice Datasets
 
-If the slice paths passed to the `zappend` tool are given as URIs,
-additional storage options may be provided for the filesystem given by the 
-URI's protocol. They may be specified using the `slice_storage_options` setting.
+A _slice dataset_ is the dataset that is appended for every slice item passed 
+to `zappend`. Slice datasets can be provided in various ways.
+
+* When using the `zappend` CLI command, slice datasets are passed as local file path 
+  or URI argument.
+
+* When using the [`zappend`](api.md) Python function, slice datasets are passed 
+  using the `slices` argument, which must be iterable. You can pass a list or tuple
+  of slice items or provide a generator that provides the slice items.
+
+Each slice item can be a local file path or URI of type `str` or `FileObj`, 
+a dataset of type `xarray.Dataset`, or a `SliceSource` object explained in more detail 
+below.
+
+#### Paths and URIs
+
+A slice object of type `str` is interpreted as local file path or URI, in the case 
+the path has a protocol prefix, such as `s3://` as described above.
+
+In the majority of `zappend` use cases the slice datasets to be appended to a target 
+dataset are passed as local file paths or URIs. A slice URI starts with a protocol 
+prefix, such as `s3://`, or `memory://`. Additional storage options may be required
+for the filesystem given by the URI's protocol. They may be specified using the
+`slice_storage_options` setting.
+
+```json
+{
+    "slice_storage_options": {
+        "anon": true
+    }
+}
+```
 
 Sometimes, the slice dataset to be processed are not yet available, e.g., 
 because another process is currently generating them. For such cases, the 
@@ -563,82 +592,29 @@ Or use default polling:
 }
 ```
 
-
-### Slice Sources
-
-A _slice source_ is an object that provides a slice dataset of type `xarray.Dataset` 
-for given parameters of any type.
-
-The optional `slice_source` configuration setting is used to specify a custom 
-slice source. If not specified, `zappend` selects the slice source based on the type 
-of a given slice object. These types are described in following subsections.
-
-If given, the value of the `slice_source` setting is a class derived from
-`zappend.api.SliceSource`, or a function that creates an instance of 
-`zappend.api.SliceSource`, or the fully qualified name of the aforementioned. 
-In the case `slice_source` is given, the _slices_ argument passed to the CLI 
-command and Python function become parameters to the specified class constructor 
-or factory function.
-The individual slice items in the `SLICES` arguments of the `zappend` CLI 
-command are of type `str`, typically interpreted as file paths or URIs.
-The individual slice items passed in the `slices` argument of the
-`zappend.api.zappend()` function can be of any type, but the `tuple`, `list`, 
-and `dict` types have a special meaning:
-
-* `tuple`: a pair of the form `(args, kwargs)`, where `args` is a list
-  or tuple of positional arguments and `kwargs` is a dictionary of keyword
-  arguments;
-  * `list`: positional arguments only;
-* `dict`: keyword arguments only;
-* Any other type is interpreted as single positional argument.
-
-In addition, your class constructor or factory function specified by `slice_source` 
-may specify a positional or keyword argument named `ctx`, which will receive the 
-current processing context of type `zappend.api.Context`. 
-
-If the `slice_source` setting is _not_ specified, the slice items passed as `slices`
-argument to the [`zappend`](api.md) Python function can be one of the types described
-in the following subsections.
-
-#### Types `str` and `FileObj`
-
-A slice object of type `str` is interpreted as local file path or URI, in the case 
-the path has a protocol prefix, such as `s3://`.
-
 An alternative to providing the slice dataset as path or URI is using the 
 `zappend.api.FileObj` class, which combines a URI with dedicated filesystem 
 storage options.
 
-```python
-from zappend.api import FileObj
-
-slice_obj = FileObj(slice_uri, storage_options=dict(...)) 
-```
-
-#### Type `Dataset`
-
-In-memory slice objects can be passed as [`xarray.Dataset`](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html) 
-objects. Such objects may originate from opening datasets from some storage 
-
-```python
-import xarray as xr
+#### Dataset Objects
 
-slice_obj = xr.open_dataset(slice_store, ...) 
-```
+You can also use dataset objects of type 
+[`xarray.Dataset`](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html) 
+as slice item. Such objects may originate from opening datasets from some storage, e.g., 
+`xarray.open_dataset(slice_store, ...)` or by composing, aggregating, resampling 
+slice datasets from other datasets and data variables.
 
-or by composing, aggregating, resampling slice datasets from other datasets and 
-data variables. To allow for out-of-core computation of large datasets [Dask arrays](https://docs.dask.org/en/stable/array.html)
-are used by both `xarray` and `zarr`. As a dask array may represent complex and/or 
+Chunked data arrays of an `xarray.Dataset` are usually instances of 
+[Dask arrays](https://docs.dask.org/en/stable/array.html), to allow for out-of-core 
+computation of large datasets. As a dask array may represent complex and/or 
 expensive processing graphs, high CPU loads and memory consumption are common issues
 for computed slice datasets, especially if the specified target dataset chunking is 
 different from the slice dataset chunking. This may cause Dask graphs to be 
 computed multiple times if the source chunking overlaps multiple target chunks, 
 potentially causing large resource overheads while recomputing and/or reloading the same
-source chunks multiple times.
-
-In such cases it can help to "terminate" such computations for each slice by 
-persisting the computed dataset first and then to reopen it. This can be specified 
-using the `persist_mem_slice` setting: 
+source chunks multiple times. In such cases it can help to "terminate" such 
+computations for each slice by persisting the computed dataset first and then to 
+reopen it. This can be specified using the `persist_mem_slice` setting: 
 
 ```json
 {
@@ -650,34 +626,82 @@ If the flag is set, in-memory slices will be persisted to a temporary Zarr befor
 appending them to the target dataset. It may prevent expensive re-computation of chunks 
 at the cost of additional i/o. It therefore defaults to `false`.
 
-#### Type `SliceSource`
+#### Slice Sources
+
+If you need some custom cleanup after a slice has been processed and appended to the 
+target dataset, you can use an instance of `zappend.api.SliceSource` as slice item.
+A `SliceSource` class requires you to implement two methods:
+
+* `get_dataset()` to return the slice dataset of type `xarray.Dataset`, and 
+* `dispose()` to perform any resource cleanup tasks. 
+
+Here is the template code for your own slice source implementation:
+
+```python
+import xarray as xr
+from zappend.api import SliceSource
+
+class MySliceSource(SliceSource):
+    # Pass any positional and keyword arguments that you need 
+    # to the constructor. `path` is just an example.
+    def __init__(self, path: str):
+        self.path = path
+        self.ds = None
+
+    def get_dataset(self) -> xr.Dataset:
+        # Write code here that obtains the dataset.
+        self.ds = xr.open_dataset(self.path)
+        # You can put any processing here.
+        return self.ds
 
-Often you want to perform some custom cleanup after a slice has been processed and
-appended to the target dataset. In this case you can write your own 
-`zappend.api.SliceSource` by implementing its `get_dataset()` and `dispose()`
-methods. 
+    def dispose(self):
+        # Write code here that performs cleanup.
+        if self.ds is not None:
+            self.ds.close()
+            self.ds = None    
+```
 
-Slice source instances are supposed to be created by _slice factories_, see 
-subsection below.
+Instead of providing instances of `SliceSource` directly as a slice item, it is often
+easier to pass your `SliceSource` class and let `zappend` pass the slice item as 
+arguments(s) to your `SliceSource`'s constructor. This can be achieved using the 
+the `slice_source` configuration setting.
 
-#### Type `SliceFactory`
+```json
+{
+    "slice_source": "mymodule.MySliceSource"
+}
+```
 
-A slice factory is a 1-argument function that receives a processing context of type
-`zappend.api.Context` and yields a slice dataset object of one of the types
-described above. Since a slice factory cannot have additional arguments, it is 
-normally defined as a [closure](https://en.wikipedia.org/wiki/Closure_(computer_programming)) 
-to capture slice-specific information.
+The `slice_source` setting can actually be **any Python function** that returns a 
+valid slice item as described above.
 
-In the following example, the actual slice dataset is computed from averaging another 
-dataset. A `SliceSource` is used to close the datasets after the slice has been 
-processed. Slice factories are created from the custom slice source and the slice paths
-using the utility function [to_slice_factories()][zappend.slice.factory.to_slice_factories]:
+If a slice source is configured, each slice item passed to `zappend` is passed as 
+argument to your slice source.
+
+* Slices passed to the `zappend` CLI command become slice source arguments 
+  of type `str`.
+* Slice items passed to the `zappend` function via the `slices` argument can be of 
+  any type, but the `tuple`, `list`, and `dict` types have a special meaning:
+
+    - `tuple`: a pair of the form `(args, kwargs)`, where `args` is a list
+      or tuple of positional arguments and `kwargs` is a dictionary of keyword
+      arguments;
+    - `list`: positional arguments only;
+    - `dict`: keyword arguments only;
+    - Any other type is interpreted as single positional argument.
+
+In addition, your slice source function or class constructor specified by `slice_source` 
+may define a 1st positional argument or keyword argument named `ctx`, 
+which will receive the current processing context of type `zappend.api.Context`. 
+This can be useful if you need to read configuration settings.
+
+Here is a more advanced example of a slice source that opens datasets from a given 
+file path and averages the values first along the time dimension:
 
 ```python
 import numpy as np
 import xarray as xr
 from zappend.api import SliceSource
-from zappend.api import to_slice_factories
 from zappend.api import zappend
 
 def get_mean_time(slice_ds: xr.Dataset) -> xr.DataArray:
@@ -690,37 +714,25 @@ def get_mean_time(slice_ds: xr.Dataset) -> xr.DataArray:
 
 def get_mean_slice(slice_ds: xr.Dataset) -> xr.Dataset: 
     mean_slice_ds = slice_ds.mean("time")
+    # Re-introduce time dimension of size one
     mean_slice_ds = mean_slice_ds.expand_dims("time", axis=0)
     mean_slice_ds.coords["time"] = get_mean_time(slice_ds)
     return mean_slice_ds 
 
 class MySliceSource(SliceSource):
-    def __init__(self, ctx, slice_path):
-        super().__init__(ctx)
+    def __init__(self, slice_path):
         self.slice_path = slice_path
         self.ds = None
-        self.mean_ds = None
 
     def get_dataset(self):
         self.ds = xr.open_dataset(self.slice_path)
-        self.mean_ds = get_mean_slice(self.ds)
-        return self.mean_ds
+        return get_mean_slice(self.ds)
 
     def dispose(self):
         if self.ds is not None:
             self.ds.close()
             self.ds = None
-        if self.mean_ds is not None:
-            self.mean_ds.close()
-            self.mean_ds = None
-        
-zappend(to_slice_factories(MySliceSource, ["slice-1.nc", "slice-2.nc", "slice-3.nc"]),
-        target_dir="target.zarr")
-```
-
-Note, the above example can be simplified by using the `slice_source` setting directly:
 
-```python
 zappend(["slice-1.nc", "slice-2.nc", "slice-3.nc"],
         target_dir="target.zarr",
         slice_source=MySliceSource)

tests/config/test_config.py

@@ -169,12 +169,15 @@ def new_custom_slice_source(ctx: Context, index: int):
 
 class CustomSliceSource(SliceSource):
     def __init__(self, ctx: Context, index: int):
-        super().__init__(ctx)
+        self.ctx = ctx
         self.index = index
 
     def get_dataset(self) -> xr.Dataset:
         return make_test_dataset(index=self.index)
 
+    def dispose(self):
+        pass
+
     @staticmethod
     def new1(ctx: Context, index: int):
         return CustomSliceSource(ctx, index)

tests/test_api.py

@@ -159,8 +159,8 @@ def test_some_slices_with_class_slice_source(self):
         )
 
     def test_some_slices_with_func_slice_source(self):
-        def process_slice(ctx, slice_ds: xr.Dataset) -> SliceSource:
-            return MySliceSource(ctx, slice_ds)
+        def process_slice(slice_ds: xr.Dataset) -> SliceSource:
+            return MySliceSource(slice_ds)
 
         target_dir = "memory://target.zarr"
         slices = [make_test_dataset(index=3 * i) for i in range(3)]
@@ -394,9 +394,11 @@ def test_some_slices_with_profiling(self):
 
 
 class MySliceSource(SliceSource):
-    def __init__(self, ctx, slice_ds):
-        super().__init__(ctx)
+    def __init__(self, slice_ds):
         self.slice_ds = slice_ds
 
     def get_dataset(self) -> xr.Dataset:
         return self.slice_ds.drop_vars(["tsm"])
+
+    def dispose(self):
+        pass